The diagnosis and prognosis of ocular surface diseases, such as keratoconjunctivitis sicca (dry eye), involves several tests. One of the tests includes staining the ocular surface with a dye, followed by visual examination for stained surface defects. Lesions in the epithelial cell layers of the cornea adsorb stain and allow for the penetration of stain into interstitial spaces. The resultant bright speckled appearance of the otherwise black cornea is a hallmark pathology of cornea disease. Visual examination has been used since the early 1900s as a diagnostic for surface eye disease. However, the variability and reliability of these assessments has been poor when performed by individual physicians. Hence there is an urgent need for a consistent measure of surface staining that is free of human bias and error. Heretofore, quantitative image analysis of ocular surface images has proven difficult for at least three reasons.
The first reason relates to Purkinje images of the illumination source in the images of the cornea and conjunctiva. The Purkinje image invariably interferes with image analysis and feature detection. Since it is not constant from one image to the next, such specular images are impossible to control for and must be removed prior to image analysis. Spectral methods are routinely used in fluorescence microscopy and whole body imaging to block undesired reflected light. Novitskaya et al. and Dean et al. described the use of a retinal camera having filtering properties for ocular surface stain imaging (Novitskaya et al., 2007, Contact Lens & Anterior Eye 30:258-259; Dean et al., 2008, Clinical and Experimental Ophthalmology 36:113-118). Novitskaya et al. and Dean et al. used digital fundus camera equipped with two filters, a blue exciter filter and a green bandpass filter, to detect fluorescence emitted from a fluorescein stained cornea. The fundus camera is designed to image fluorescein injected in to the vasculature as a means to assess retinal blood flow. The spectral properties of these filters were not described.
A second difficulty in accurately detecting and measuring corneal stain relates to the methods used to capture corneal images. Any object in an image that is to be measured has to be in sharp focus in order to visually or mathematically delineate the boundaries of the object. Stained objects on the corneal surface reside on a highly curved surface and require the use of methods that increase the depth of focus or depth of field in the image. The average height of the cornea from the limbal edge to the apex, also called the sag height is 2.8 millimeters for the human eye. The standard of care diagnostic system for viewing and imaging the cornea uses a biomicroscope on a slit lamp that has a depth of field of 0.7-0.8 mm on average. Hence any given stained corneal image from such a system can not possibly have all stained objects in sharp focus for subsequent analysis. Other imaging systems such as the retinal camera described by Novitskaya et al. and Dean et al. have a similar narrow depth of field for adequate image capture of the stained cornea.
A third difficulty in accurately detecting and measuring corneal stain relates to the methods used to separate surface stained objects from surrounding image elements for subsequent measurement. Images of stained ocular surfaces from patients with dry eye or other ocular surface diseases generally have bright punctate stained objects of interest amidst a background fluorescence that is an unwanted signal and is not to be included in the measurement.
Consequently, there is a need in the art to eliminate unwanted specular images in corneal staining and to capture full field in focus images of the corneal surface while enhancing a light signal that represents ocular surface defects for accurate diagnosis and monitoring of corneal surface diseases.